Overview

Dataset statistics

Number of variables39
Number of observations1582
Missing cells7203
Missing cells (%)11.7%
Duplicate rows94
Duplicate rows (%)5.9%
Total size in memory482.1 KiB
Average record size in memory312.1 B

Variable types

DateTime5
Numeric7
Categorical23
Unsupported3
Boolean1

Alerts

LOB has constant value "FMCG" Constant
BP TYPE has constant value "RENTAL" Constant
Transfer Type has constant value "Pallet CompanyOUT" Constant
PRODUCT CATEGORY has constant value "Wooden Pallet" Constant
UNIT has constant value "Nos" Constant
Document Type has constant value "Allot" Constant
Detention has constant value "0.0" Constant
Dataset has 94 (5.9%) duplicate rowsDuplicates
Customer/Vendor Name has a high cardinality: 143 distinct values High cardinality
City has a high cardinality: 62 distinct values High cardinality
TO WhsName has a high cardinality: 143 distinct values High cardinality
SO ID has a high cardinality: 424 distinct values High cardinality
Customer/Vendor Code is highly correlated with Region and 7 other fieldsHigh correlation
To whsCode is highly correlated with Customer/Vendor Code and 7 other fieldsHigh correlation
Region is highly correlated with Customer/Vendor Code and 6 other fieldsHigh correlation
From WhsCode is highly correlated with Customer/Vendor Code and 12 other fieldsHigh correlation
U_ActShipType is highly correlated with Customer/Vendor Code and 3 other fieldsHigh correlation
ItemCode is highly correlated with Model TYPE and 5 other fieldsHigh correlation
BP TYPE is highly correlated with Region and 19 other fieldsHigh correlation
Detention is highly correlated with Region and 19 other fieldsHigh correlation
U_SOTYPE is highly correlated with Model TYPE and 4 other fieldsHigh correlation
U_AssetClass is highly correlated with ItemCode and 2 other fieldsHigh correlation
UNIT is highly correlated with Region and 19 other fieldsHigh correlation
LOB is highly correlated with Region and 19 other fieldsHigh correlation
U_DocStatus is highly correlated with From WhsCodeHigh correlation
Direct Dispatch is highly correlated with From WhsCode and 2 other fieldsHigh correlation
STATE is highly correlated with Customer/Vendor Code and 7 other fieldsHigh correlation
From WhsName is highly correlated with Customer/Vendor Code and 8 other fieldsHigh correlation
City is highly correlated with Customer/Vendor Code and 9 other fieldsHigh correlation
Document Type is highly correlated with Region and 19 other fieldsHigh correlation
Vehicle Type is highly correlated with Region and 11 other fieldsHigh correlation
BP CATEGORY is highly correlated with Customer/Vendor Code and 1 other fieldsHigh correlation
PRODUCT CATEGORY is highly correlated with Region and 19 other fieldsHigh correlation
Transfer Type is highly correlated with Region and 19 other fieldsHigh correlation
Model TYPE is highly correlated with ItemCode and 3 other fieldsHigh correlation
QUANTITY is highly correlated with City and 5 other fieldsHigh correlation
RATE is highly correlated with Model TYPE and 4 other fieldsHigh correlation
U_GRNNO is highly correlated with City and 1 other fieldsHigh correlation
Loading/Unloading is highly correlated with City and 3 other fieldsHigh correlation
NumAtCard has 1582 (100.0%) missing values Missing
Comments has 1549 (97.9%) missing values Missing
U_GRNNO has 861 (54.4%) missing values Missing
Loading/Unloading has 487 (30.8%) missing values Missing
Detention has 1138 (71.9%) missing values Missing
KITITEM has 1582 (100.0%) missing values Missing
NumAtCard is an unsupported type, check if it needs cleaning or further analysis Unsupported
Comments is an unsupported type, check if it needs cleaning or further analysis Unsupported
KITITEM is an unsupported type, check if it needs cleaning or further analysis Unsupported
U_Frt has 281 (17.8%) zeros Zeros
Loading/Unloading has 1065 (67.3%) zeros Zeros

Reproduction

Analysis started2023-04-30 17:17:52.591326
Analysis finished2023-04-30 17:18:06.783848
Duration14.19 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Distinct479
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Minimum2019-12-06 00:00:00
Maximum2022-11-26 00:00:00
2023-04-30T22:48:06.902570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:07.098009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct472
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Minimum2019-12-06 00:00:00
Maximum2022-11-26 00:00:00
2023-04-30T22:48:07.270589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:07.444084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct479
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Minimum2019-12-06 00:00:00
Maximum2022-11-26 00:00:00
2023-04-30T22:48:07.791187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:07.964733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Customer/Vendor Code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct143
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10004860.78
Minimum10000408
Maximum10008338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2023-04-30T22:48:08.136232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10000408
5-th percentile10001316
Q110002425
median10006243
Q310006524
95-th percentile10006966.9
Maximum10008338
Range7930
Interquartile range (IQR)4099

Descriptive statistics

Standard deviation2238.812447
Coefficient of variation (CV)0.0002237724738
Kurtosis-1.390454343
Mean10004860.78
Median Absolute Deviation (MAD)480
Skewness-0.615446634
Sum1.582768975 × 1010
Variance5012281.171
MonotonicityNot monotonic
2023-04-30T22:48:08.300820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000243087
 
5.5%
1000624367
 
4.2%
1000633463
 
4.0%
1000645351
 
3.2%
1000239150
 
3.2%
1000587148
 
3.0%
1000137845
 
2.8%
1000633542
 
2.7%
1000243642
 
2.7%
1000667839
 
2.5%
Other values (133)1048
66.2%
ValueCountFrequency (%)
100004088
 
0.5%
100004111
 
0.1%
100004132
 
0.1%
100004621
 
0.1%
100007407
 
0.4%
1000115520
1.3%
1000121629
1.8%
1000131614
0.9%
100013174
 
0.3%
1000131915
0.9%
ValueCountFrequency (%)
100083381
 
0.1%
100083145
 
0.3%
100081121
 
0.1%
100080091
 
0.1%
100079442
 
0.1%
1000780919
1.2%
100077352
 
0.1%
100074802
 
0.1%
100073536
 
0.4%
1000719932
2.0%

Customer/Vendor Name
Categorical

HIGH CARDINALITY

Distinct143
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Cart Services Pvt. Ltd. _ Kolkata _Bagnan
 
87
Cart Services Pvt. Ltd. _ Coimbatore_ Selakarichal
 
67
Cart Services Private Limited_Rewari_Khijuri
 
63
Cart Services Private Limited_Bhiwandi_Saidham_G Whse_Unit 2
 
51
Cart Services Pvt. Ltd. _ Bangalore _Venkatapura Village Malur
 
50
Other values (138)
1264 

Length

Max length70
Median length56
Mean length45.63337547
Min length28

Characters and Unicode

Total characters72192
Distinct characters62
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)2.7%

Sample

1st rowCart Services Pvt. Ltd. _ Howrah _Uluberia ESR Warehousing
2nd rowCart Services Pvt. Ltd. _ Howrah _Uluberia ESR Warehousing
3rd rowCart Services Pvt Ltd _ Gurgoan _Embassy Industrial
4th rowCart Services Pvt. Ltd. _ Gurgoan _ Tauru
5th rowCart Services Pvt. Ltd. _ Gurgoan _ Tauru

Common Values

ValueCountFrequency (%)
Cart Services Pvt. Ltd. _ Kolkata _Bagnan87
 
5.5%
Cart Services Pvt. Ltd. _ Coimbatore_ Selakarichal67
 
4.2%
Cart Services Private Limited_Rewari_Khijuri63
 
4.0%
Cart Services Private Limited_Bhiwandi_Saidham_G Whse_Unit 251
 
3.2%
Cart Services Pvt. Ltd. _ Bangalore _Venkatapura Village Malur50
 
3.2%
Cart Services Pvt. Ltd. _Hosur48
 
3.0%
Cart Services Pvt. Ltd. _ Ludhiana45
 
2.8%
Cart Services Private Limited_Thiruvallur_Orakkadu42
 
2.7%
Cart Services Pvt. Ltd. _ Hosur _U2 BTS Large42
 
2.7%
Cart Services Private Limited_Haringhata39
 
2.5%
Other values (133)1048
66.2%

Length

2023-04-30T22:48:08.472334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cart1576
15.4%
services1576
15.4%
1174
11.4%
pvt1069
 
10.4%
ltd1017
 
9.9%
private513
 
5.0%
limited164
 
1.6%
bangalore150
 
1.5%
bhiwandi114
 
1.1%
hyderabad96
 
0.9%
Other values (204)2811
27.4%

Most occurring characters

ValueCountFrequency (%)
8826
 
12.2%
a6543
 
9.1%
r5490
 
7.6%
e5489
 
7.6%
t5430
 
7.5%
i4870
 
6.7%
v3298
 
4.6%
_3110
 
4.3%
d2563
 
3.6%
.2056
 
2.8%
Other values (52)24517
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter47282
65.5%
Uppercase Letter10543
 
14.6%
Space Separator8826
 
12.2%
Connector Punctuation3110
 
4.3%
Other Punctuation2071
 
2.9%
Decimal Number360
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a6543
13.8%
r5490
11.6%
e5489
11.6%
t5430
11.5%
i4870
10.3%
v3298
 
7.0%
d2563
 
5.4%
s1960
 
4.1%
c1716
 
3.6%
n1399
 
3.0%
Other values (15)8524
18.0%
Uppercase Letter
ValueCountFrequency (%)
S1931
18.3%
C1867
17.7%
L1785
16.9%
P1676
15.9%
B614
 
5.8%
H384
 
3.6%
K255
 
2.4%
M240
 
2.3%
G210
 
2.0%
F184
 
1.7%
Other values (15)1397
13.3%
Decimal Number
ValueCountFrequency (%)
2170
47.2%
160
 
16.7%
834
 
9.4%
033
 
9.2%
326
 
7.2%
620
 
5.6%
414
 
3.9%
93
 
0.8%
Other Punctuation
ValueCountFrequency (%)
.2056
99.3%
,15
 
0.7%
Space Separator
ValueCountFrequency (%)
8826
100.0%
Connector Punctuation
ValueCountFrequency (%)
_3110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin57825
80.1%
Common14367
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a6543
 
11.3%
r5490
 
9.5%
e5489
 
9.5%
t5430
 
9.4%
i4870
 
8.4%
v3298
 
5.7%
d2563
 
4.4%
s1960
 
3.4%
S1931
 
3.3%
C1867
 
3.2%
Other values (40)18384
31.8%
Common
ValueCountFrequency (%)
8826
61.4%
_3110
 
21.6%
.2056
 
14.3%
2170
 
1.2%
160
 
0.4%
834
 
0.2%
033
 
0.2%
326
 
0.2%
620
 
0.1%
,15
 
0.1%
Other values (2)17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII72192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8826
 
12.2%
a6543
 
9.1%
r5490
 
7.6%
e5489
 
7.6%
t5430
 
7.5%
i4870
 
6.7%
v3298
 
4.6%
_3110
 
4.3%
d2563
 
3.6%
.2056
 
2.8%
Other values (52)24517
34.0%

LOB
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
FMCG
1582 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters6328
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFMCG
2nd rowFMCG
3rd rowFMCG
4th rowFMCG
5th rowFMCG

Common Values

ValueCountFrequency (%)
FMCG1582
100.0%

Length

2023-04-30T22:48:08.610996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:08.732679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
fmcg1582
100.0%

Most occurring characters

ValueCountFrequency (%)
F1582
25.0%
M1582
25.0%
C1582
25.0%
G1582
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6328
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F1582
25.0%
M1582
25.0%
C1582
25.0%
G1582
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6328
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F1582
25.0%
M1582
25.0%
C1582
25.0%
G1582
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F1582
25.0%
M1582
25.0%
C1582
25.0%
G1582
25.0%

Region
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
South
618 
North
361 
East
305 
West
298 

Length

Max length5
Median length5
Mean length4.618836915
Min length4

Characters and Unicode

Total characters7307
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEast
2nd rowEast
3rd rowNorth
4th rowNorth
5th rowNorth

Common Values

ValueCountFrequency (%)
South618
39.1%
North361
22.8%
East305
19.3%
West298
18.8%

Length

2023-04-30T22:48:08.836387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:08.970046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
south618
39.1%
north361
22.8%
east305
19.3%
west298
18.8%

Most occurring characters

ValueCountFrequency (%)
t1582
21.7%
o979
13.4%
h979
13.4%
S618
 
8.5%
u618
 
8.5%
s603
 
8.3%
N361
 
4.9%
r361
 
4.9%
E305
 
4.2%
a305
 
4.2%
Other values (2)596
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5725
78.3%
Uppercase Letter1582
 
21.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t1582
27.6%
o979
17.1%
h979
17.1%
u618
 
10.8%
s603
 
10.5%
r361
 
6.3%
a305
 
5.3%
e298
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
S618
39.1%
N361
22.8%
E305
19.3%
W298
18.8%

Most occurring scripts

ValueCountFrequency (%)
Latin7307
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t1582
21.7%
o979
13.4%
h979
13.4%
S618
 
8.5%
u618
 
8.5%
s603
 
8.3%
N361
 
4.9%
r361
 
4.9%
E305
 
4.2%
a305
 
4.2%
Other values (2)596
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII7307
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t1582
21.7%
o979
13.4%
h979
13.4%
S618
 
8.5%
u618
 
8.5%
s603
 
8.3%
N361
 
4.9%
r361
 
4.9%
E305
 
4.2%
a305
 
4.2%
Other values (2)596
 
8.2%

BP TYPE
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
RENTAL
1582 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters9492
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENTAL
2nd rowRENTAL
3rd rowRENTAL
4th rowRENTAL
5th rowRENTAL

Common Values

ValueCountFrequency (%)
RENTAL1582
100.0%

Length

2023-04-30T22:48:09.098701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:09.220375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
rental1582
100.0%

Most occurring characters

ValueCountFrequency (%)
R1582
16.7%
E1582
16.7%
N1582
16.7%
T1582
16.7%
A1582
16.7%
L1582
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter9492
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R1582
16.7%
E1582
16.7%
N1582
16.7%
T1582
16.7%
A1582
16.7%
L1582
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin9492
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R1582
16.7%
E1582
16.7%
N1582
16.7%
T1582
16.7%
A1582
16.7%
L1582
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII9492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R1582
16.7%
E1582
16.7%
N1582
16.7%
T1582
16.7%
A1582
16.7%
L1582
16.7%

City
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct62
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Bangalore
159 
Bhiwandi
111 
Thane
102 
Hyderabad
 
100
Kolkata
 
92
Other values (57)
1018 

Length

Max length13
Median length11
Mean length7.627054362
Min length4

Characters and Unicode

Total characters12066
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.9%

Sample

1st rowHowrah
2nd rowHowrah
3rd rowGurgaon
4th rowGurgaon
5th rowGurgaon

Common Values

ValueCountFrequency (%)
Bangalore159
 
10.1%
Bhiwandi111
 
7.0%
Thane102
 
6.4%
Hyderabad100
 
6.3%
Kolkata92
 
5.8%
Hosur90
 
5.7%
Coimbatore85
 
5.4%
Gurgaon82
 
5.2%
Rewari63
 
4.0%
Chennai61
 
3.9%
Other values (52)637
40.3%

Length

2023-04-30T22:48:09.333075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bangalore159
 
10.0%
bhiwandi111
 
7.0%
thane102
 
6.4%
hyderabad100
 
6.3%
kolkata92
 
5.8%
hosur90
 
5.7%
coimbatore85
 
5.4%
gurgaon82
 
5.2%
rewari63
 
4.0%
chennai61
 
3.8%
Other values (52)640
40.4%

Most occurring characters

ValueCountFrequency (%)
a2200
18.2%
r996
 
8.3%
n864
 
7.2%
o825
 
6.8%
i733
 
6.1%
h700
 
5.8%
e630
 
5.2%
d516
 
4.3%
u454
 
3.8%
g438
 
3.6%
Other values (36)3710
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10481
86.9%
Uppercase Letter1582
 
13.1%
Space Separator3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2200
21.0%
r996
9.5%
n864
 
8.2%
o825
 
7.9%
i733
 
7.0%
h700
 
6.7%
e630
 
6.0%
d516
 
4.9%
u454
 
4.3%
g438
 
4.2%
Other values (14)2125
20.3%
Uppercase Letter
ValueCountFrequency (%)
H300
19.0%
B281
17.8%
C147
9.3%
T144
9.1%
G118
 
7.5%
K102
 
6.4%
D70
 
4.4%
L68
 
4.3%
R67
 
4.2%
J51
 
3.2%
Other values (11)234
14.8%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12063
> 99.9%
Common3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2200
18.2%
r996
 
8.3%
n864
 
7.2%
o825
 
6.8%
i733
 
6.1%
h700
 
5.8%
e630
 
5.2%
d516
 
4.3%
u454
 
3.8%
g438
 
3.6%
Other values (35)3707
30.7%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2200
18.2%
r996
 
8.3%
n864
 
7.2%
o825
 
6.8%
i733
 
6.1%
h700
 
5.8%
e630
 
5.2%
d516
 
4.3%
u454
 
3.8%
g438
 
3.6%
Other values (36)3710
30.7%

STATE
Categorical

HIGH CORRELATION

Distinct18
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Tamil Nadu
282 
Maharashtra
257 
Haryana
230 
West Bengal
219 
Karnataka
199 
Other values (13)
395 

Length

Max length14
Median length13
Mean length9.415929204
Min length5

Characters and Unicode

Total characters14896
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest Bengal
2nd rowWest Bengal
3rd rowHaryana
4th rowHaryana
5th rowHaryana

Common Values

ValueCountFrequency (%)
Tamil Nadu282
17.8%
Maharashtra257
16.2%
Haryana230
14.5%
West Bengal219
13.8%
Karnataka199
12.6%
Telangana109
 
6.9%
Uttar Pradesh68
 
4.3%
Punjab56
 
3.5%
Bihar45
 
2.8%
Gujarat31
 
2.0%
Other values (8)86
 
5.4%

Length

2023-04-30T22:48:09.461718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tamil282
12.9%
nadu282
12.9%
maharashtra257
11.8%
haryana230
10.5%
west219
10.0%
bengal219
10.0%
karnataka199
9.1%
telangana109
 
5.0%
pradesh103
 
4.7%
uttar68
 
3.1%
Other values (11)218
10.0%

Most occurring characters

ValueCountFrequency (%)
a4061
27.3%
r1223
 
8.2%
n956
 
6.4%
t852
 
5.7%
h723
 
4.9%
s655
 
4.4%
e653
 
4.4%
l613
 
4.1%
604
 
4.1%
d431
 
2.9%
Other values (24)4125
27.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12106
81.3%
Uppercase Letter2186
 
14.7%
Space Separator604
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a4061
33.5%
r1223
 
10.1%
n956
 
7.9%
t852
 
7.0%
h723
 
6.0%
s655
 
5.4%
e653
 
5.4%
l613
 
5.1%
d431
 
3.6%
u369
 
3.0%
Other values (7)1570
 
13.0%
Uppercase Letter
ValueCountFrequency (%)
T391
17.9%
N282
12.9%
M264
12.1%
B264
12.1%
H230
10.5%
W219
10.0%
K199
9.1%
P159
7.3%
U68
 
3.1%
A58
 
2.7%
Other values (6)52
 
2.4%
Space Separator
ValueCountFrequency (%)
604
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14292
95.9%
Common604
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a4061
28.4%
r1223
 
8.6%
n956
 
6.7%
t852
 
6.0%
h723
 
5.1%
s655
 
4.6%
e653
 
4.6%
l613
 
4.3%
d431
 
3.0%
T391
 
2.7%
Other values (23)3734
26.1%
Common
ValueCountFrequency (%)
604
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII14896
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a4061
27.3%
r1223
 
8.2%
n956
 
6.4%
t852
 
5.7%
h723
 
4.9%
s655
 
4.4%
e653
 
4.4%
l613
 
4.1%
604
 
4.1%
d431
 
2.9%
Other values (24)4125
27.7%

From WhsCode
Categorical

HIGH CORRELATION

Distinct34
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
KA02
357 
MH03
200 
HY01
147 
WB01
121 
MH01
114 
Other values (29)
643 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters6328
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowWB01
2nd rowWB01
3rd rowHY01
4th rowHY01
5th rowHY01

Common Values

ValueCountFrequency (%)
KA02357
22.6%
MH03200
12.6%
HY01147
9.3%
WB01121
 
7.6%
MH01114
 
7.2%
WB0490
 
5.7%
TL0273
 
4.6%
TN0266
 
4.2%
HY0259
 
3.7%
TL0147
 
3.0%
Other values (24)308
19.5%

Length

2023-04-30T22:48:09.585390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ka02357
22.6%
mh03200
12.6%
hy01147
9.3%
wb01121
 
7.6%
mh01114
 
7.2%
wb0490
 
5.7%
tl0273
 
4.6%
tn0266
 
4.2%
hy0259
 
3.7%
tl0147
 
3.0%
Other values (24)308
19.5%

Most occurring characters

ValueCountFrequency (%)
01582
25.0%
2629
 
9.9%
H625
 
9.9%
1502
 
7.9%
M375
 
5.9%
A372
 
5.9%
K363
 
5.7%
Y256
 
4.0%
T238
 
3.8%
3235
 
3.7%
Other values (16)1151
18.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3164
50.0%
Uppercase Letter3164
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H625
19.8%
M375
11.9%
A372
11.8%
K363
11.5%
Y256
8.1%
T238
 
7.5%
B225
 
7.1%
W218
 
6.9%
L120
 
3.8%
N111
 
3.5%
Other values (7)261
8.2%
Decimal Number
ValueCountFrequency (%)
01582
50.0%
2629
 
19.9%
1502
 
15.9%
3235
 
7.4%
4103
 
3.3%
559
 
1.9%
841
 
1.3%
68
 
0.3%
75
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common3164
50.0%
Latin3164
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H625
19.8%
M375
11.9%
A372
11.8%
K363
11.5%
Y256
8.1%
T238
 
7.5%
B225
 
7.1%
W218
 
6.9%
L120
 
3.8%
N111
 
3.5%
Other values (7)261
8.2%
Common
ValueCountFrequency (%)
01582
50.0%
2629
 
19.9%
1502
 
15.9%
3235
 
7.4%
4103
 
3.3%
559
 
1.9%
841
 
1.3%
68
 
0.3%
75
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII6328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01582
25.0%
2629
 
9.9%
H625
 
9.9%
1502
 
7.9%
M375
 
5.9%
A372
 
5.9%
K363
 
5.7%
Y256
 
4.0%
T238
 
3.8%
3235
 
3.7%
Other values (16)1151
18.2%

From WhsName
Categorical

HIGH CORRELATION

Distinct23
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Pallet Company India Bangalore-Hobli
357 
Pallet Company India Kolkata
211 
Pallet Company India Bhiwandi
205 
Pallet Company India Pataudi
147 
Pallet Company India Hyderabad
120 
Other values (18)
542 

Length

Max length41
Median length37
Mean length29.96396966
Min length23

Characters and Unicode

Total characters47403
Distinct characters39
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowPallet Company India Kolkata
2nd rowPallet Company India Kolkata
3rd rowPallet Company India Pataudi
4th rowPallet Company India Pataudi
5th rowPallet Company India Pataudi

Common Values

ValueCountFrequency (%)
Pallet Company India Bangalore-Hobli357
22.6%
Pallet Company India Kolkata211
13.3%
Pallet Company India Bhiwandi205
13.0%
Pallet Company India Pataudi147
9.3%
Pallet Company India Hyderabad120
 
7.6%
Pallet Company India HO114
 
7.2%
Pallet Company India Chennai111
 
7.0%
Pallet Company India Ambala67
 
4.2%
Pallet Company India Lucknow57
 
3.6%
Pallet Company India Nagpur39
 
2.5%
Other values (13)154
9.7%

Length

2023-04-30T22:48:09.725029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pallet1582
24.8%
company1582
24.8%
india1582
24.8%
bangalore-hobli357
 
5.6%
kolkata211
 
3.3%
bhiwandi205
 
3.2%
pataudi183
 
2.9%
hyderabad120
 
1.9%
ho114
 
1.8%
chennai111
 
1.7%
Other values (17)340
 
5.3%

Most occurring characters

ValueCountFrequency (%)
a7212
15.2%
4805
 
10.1%
l4169
 
8.8%
n4064
 
8.6%
i2751
 
5.8%
o2600
 
5.5%
d2302
 
4.9%
e2252
 
4.8%
t1991
 
4.2%
P1781
 
3.8%
Other values (29)13476
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter35311
74.5%
Uppercase Letter6888
 
14.5%
Space Separator4805
 
10.1%
Dash Punctuation370
 
0.8%
Other Punctuation23
 
< 0.1%
Decimal Number6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a7212
20.4%
l4169
11.8%
n4064
11.5%
i2751
 
7.8%
o2600
 
7.4%
d2302
 
6.5%
e2252
 
6.4%
t1991
 
5.6%
y1702
 
4.8%
p1672
 
4.7%
Other values (11)4596
13.0%
Uppercase Letter
ValueCountFrequency (%)
P1781
25.9%
C1693
24.6%
I1589
23.1%
H591
 
8.6%
B580
 
8.4%
K240
 
3.5%
O114
 
1.7%
N85
 
1.2%
G68
 
1.0%
A67
 
1.0%
Other values (4)80
 
1.2%
Space Separator
ValueCountFrequency (%)
4805
100.0%
Dash Punctuation
ValueCountFrequency (%)
-370
100.0%
Other Punctuation
ValueCountFrequency (%)
.23
100.0%
Decimal Number
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin42199
89.0%
Common5204
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a7212
17.1%
l4169
 
9.9%
n4064
 
9.6%
i2751
 
6.5%
o2600
 
6.2%
d2302
 
5.5%
e2252
 
5.3%
t1991
 
4.7%
P1781
 
4.2%
y1702
 
4.0%
Other values (25)11375
27.0%
Common
ValueCountFrequency (%)
4805
92.3%
-370
 
7.1%
.23
 
0.4%
16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII47403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a7212
15.2%
4805
 
10.1%
l4169
 
8.8%
n4064
 
8.6%
i2751
 
5.8%
o2600
 
5.5%
d2302
 
4.9%
e2252
 
4.8%
t1991
 
4.2%
P1781
 
3.8%
Other values (29)13476
28.4%

To whsCode
Real number (ℝ≥0)

HIGH CORRELATION

Distinct143
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10004860.78
Minimum10000408
Maximum10008338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2023-04-30T22:48:09.881567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10000408
5-th percentile10001316
Q110002425
median10006243
Q310006524
95-th percentile10006966.9
Maximum10008338
Range7930
Interquartile range (IQR)4099

Descriptive statistics

Standard deviation2238.812447
Coefficient of variation (CV)0.0002237724738
Kurtosis-1.390454343
Mean10004860.78
Median Absolute Deviation (MAD)480
Skewness-0.615446634
Sum1.582768975 × 1010
Variance5012281.171
MonotonicityNot monotonic
2023-04-30T22:48:10.045157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000243087
 
5.5%
1000624367
 
4.2%
1000633463
 
4.0%
1000645351
 
3.2%
1000239150
 
3.2%
1000587148
 
3.0%
1000137845
 
2.8%
1000633542
 
2.7%
1000243642
 
2.7%
1000667839
 
2.5%
Other values (133)1048
66.2%
ValueCountFrequency (%)
100004088
 
0.5%
100004111
 
0.1%
100004132
 
0.1%
100004621
 
0.1%
100007407
 
0.4%
1000115520
1.3%
1000121629
1.8%
1000131614
0.9%
100013174
 
0.3%
1000131915
0.9%
ValueCountFrequency (%)
100083381
 
0.1%
100083145
 
0.3%
100081121
 
0.1%
100080091
 
0.1%
100079442
 
0.1%
1000780919
1.2%
100077352
 
0.1%
100074802
 
0.1%
100073536
 
0.4%
1000719932
2.0%

TO WhsName
Categorical

HIGH CARDINALITY

Distinct143
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Cart Services Pvt. Ltd. - Kolkata (Bagnan)
 
87
Cart Services Pvt. Ltd. _ Coimbatore_ Selakarichal
 
67
Cart Services Private Limited_Rewari_Khijuri
 
63
Cart Services Private Limited_Bhiwandi_Saidham_G Whse_Unit 2
 
51
Cart Services Pvt. Ltd. - Bangalore (Venkatapura Village, Malur )
 
50
Other values (138)
1264 

Length

Max length72
Median length58
Mean length45.88874842
Min length28

Characters and Unicode

Total characters72596
Distinct characters66
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)2.7%

Sample

1st rowCart Services Pvt. Ltd. - Howrah (Uluberia, ESR Warehousing)
2nd rowCart Services Pvt. Ltd. - Howrah (Uluberia, ESR Warehousing)
3rd rowCart Services Pvt. Ltd. - Gurgoan (Embassy Industrial)
4th rowCart Services Pvt. Ltd. - Gurgoan ( Tauru)
5th rowCart Services Pvt. Ltd. - Gurgoan ( Tauru)

Common Values

ValueCountFrequency (%)
Cart Services Pvt. Ltd. - Kolkata (Bagnan)87
 
5.5%
Cart Services Pvt. Ltd. _ Coimbatore_ Selakarichal67
 
4.2%
Cart Services Private Limited_Rewari_Khijuri63
 
4.0%
Cart Services Private Limited_Bhiwandi_Saidham_G Whse_Unit 251
 
3.2%
Cart Services Pvt. Ltd. - Bangalore (Venkatapura Village, Malur )50
 
3.2%
Cart Services Pvt. Ltd. _Hosur48
 
3.0%
Cart Services Pvt. Ltd. - Ludhiana45
 
2.8%
Cart Services Private Limited_Thiruvallur_Orakkadu42
 
2.7%
Cart Services Pvt. Ltd. - Hosur (U2 BTS Large)42
 
2.7%
Cart Services Private Limited_Haringhata39
 
2.5%
Other values (133)1048
66.2%

Length

2023-04-30T22:48:10.225693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cart1576
15.3%
services1576
15.3%
1224
11.9%
pvt1069
 
10.4%
ltd1017
 
9.9%
private513
 
5.0%
limited164
 
1.6%
bangalore150
 
1.5%
bhiwandi114
 
1.1%
hyderabad96
 
0.9%
Other values (201)2811
27.3%

Most occurring characters

ValueCountFrequency (%)
8780
 
12.1%
a6533
 
9.0%
r5483
 
7.6%
e5476
 
7.5%
t5424
 
7.5%
i4863
 
6.7%
v3298
 
4.5%
d2556
 
3.5%
_2136
 
2.9%
.2074
 
2.9%
Other values (56)25973
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter47199
65.0%
Uppercase Letter10533
 
14.5%
Space Separator8780
 
12.1%
Other Punctuation2170
 
3.0%
Connector Punctuation2136
 
2.9%
Dash Punctuation521
 
0.7%
Open Punctuation450
 
0.6%
Close Punctuation450
 
0.6%
Decimal Number357
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a6533
13.8%
r5483
11.6%
e5476
11.6%
t5424
11.5%
i4863
10.3%
v3298
 
7.0%
d2556
 
5.4%
s1953
 
4.1%
c1716
 
3.6%
n1389
 
2.9%
Other values (15)8508
18.0%
Uppercase Letter
ValueCountFrequency (%)
S1928
18.3%
C1867
17.7%
L1791
17.0%
P1669
15.8%
B621
 
5.9%
H381
 
3.6%
K255
 
2.4%
M236
 
2.2%
G210
 
2.0%
F184
 
1.7%
Other values (15)1391
13.2%
Decimal Number
ValueCountFrequency (%)
2170
47.6%
157
 
16.0%
834
 
9.5%
033
 
9.2%
326
 
7.3%
620
 
5.6%
414
 
3.9%
93
 
0.8%
Other Punctuation
ValueCountFrequency (%)
.2074
95.6%
,93
 
4.3%
&3
 
0.1%
Space Separator
ValueCountFrequency (%)
8780
100.0%
Connector Punctuation
ValueCountFrequency (%)
_2136
100.0%
Dash Punctuation
ValueCountFrequency (%)
-521
100.0%
Open Punctuation
ValueCountFrequency (%)
(450
100.0%
Close Punctuation
ValueCountFrequency (%)
)450
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin57732
79.5%
Common14864
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a6533
 
11.3%
r5483
 
9.5%
e5476
 
9.5%
t5424
 
9.4%
i4863
 
8.4%
v3298
 
5.7%
d2556
 
4.4%
s1953
 
3.4%
S1928
 
3.3%
C1867
 
3.2%
Other values (40)18351
31.8%
Common
ValueCountFrequency (%)
8780
59.1%
_2136
 
14.4%
.2074
 
14.0%
-521
 
3.5%
(450
 
3.0%
)450
 
3.0%
2170
 
1.1%
,93
 
0.6%
157
 
0.4%
834
 
0.2%
Other values (6)99
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII72596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8780
 
12.1%
a6533
 
9.0%
r5483
 
7.6%
e5476
 
7.5%
t5424
 
7.5%
i4863
 
6.7%
v3298
 
4.5%
d2556
 
3.5%
_2136
 
2.9%
.2074
 
2.9%
Other values (56)25973
35.8%

Model TYPE
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Allot
1560 
Damage Swap
 
15
Trial
 
7

Length

Max length11
Median length5
Mean length5.056890013
Min length5

Characters and Unicode

Total characters8000
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAllot
2nd rowAllot
3rd rowAllot
4th rowAllot
5th rowAllot

Common Values

ValueCountFrequency (%)
Allot1560
98.6%
Damage Swap15
 
0.9%
Trial7
 
0.4%

Length

2023-04-30T22:48:10.374282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:10.537814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
allot1560
97.7%
damage15
 
0.9%
swap15
 
0.9%
trial7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
l3127
39.1%
A1560
19.5%
o1560
19.5%
t1560
19.5%
a52
 
0.7%
D15
 
0.2%
m15
 
0.2%
g15
 
0.2%
e15
 
0.2%
15
 
0.2%
Other values (6)66
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6388
79.8%
Uppercase Letter1597
 
20.0%
Space Separator15
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l3127
49.0%
o1560
24.4%
t1560
24.4%
a52
 
0.8%
m15
 
0.2%
g15
 
0.2%
e15
 
0.2%
w15
 
0.2%
p15
 
0.2%
r7
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
A1560
97.7%
D15
 
0.9%
S15
 
0.9%
T7
 
0.4%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7985
99.8%
Common15
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
l3127
39.2%
A1560
19.5%
o1560
19.5%
t1560
19.5%
a52
 
0.7%
D15
 
0.2%
m15
 
0.2%
g15
 
0.2%
e15
 
0.2%
S15
 
0.2%
Other values (5)51
 
0.6%
Common
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l3127
39.1%
A1560
19.5%
o1560
19.5%
t1560
19.5%
a52
 
0.7%
D15
 
0.2%
m15
 
0.2%
g15
 
0.2%
e15
 
0.2%
15
 
0.2%
Other values (6)66
 
0.8%

Transfer Type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Pallet CompanyOUT
1582 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters26894
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPallet CompanyOUT
2nd rowPallet CompanyOUT
3rd rowPallet CompanyOUT
4th rowPallet CompanyOUT
5th rowPallet CompanyOUT

Common Values

ValueCountFrequency (%)
Pallet CompanyOUT1582
100.0%

Length

2023-04-30T22:48:10.732295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:10.963677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
pallet1582
50.0%
companyout1582
50.0%

Most occurring characters

ValueCountFrequency (%)
a3164
 
11.8%
l3164
 
11.8%
P1582
 
5.9%
e1582
 
5.9%
t1582
 
5.9%
1582
 
5.9%
C1582
 
5.9%
o1582
 
5.9%
m1582
 
5.9%
p1582
 
5.9%
Other values (5)7910
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17402
64.7%
Uppercase Letter7910
29.4%
Space Separator1582
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a3164
18.2%
l3164
18.2%
e1582
9.1%
t1582
9.1%
o1582
9.1%
m1582
9.1%
p1582
9.1%
n1582
9.1%
y1582
9.1%
Uppercase Letter
ValueCountFrequency (%)
P1582
20.0%
C1582
20.0%
O1582
20.0%
U1582
20.0%
T1582
20.0%
Space Separator
ValueCountFrequency (%)
1582
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25312
94.1%
Common1582
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a3164
12.5%
l3164
12.5%
P1582
 
6.2%
e1582
 
6.2%
t1582
 
6.2%
C1582
 
6.2%
o1582
 
6.2%
m1582
 
6.2%
p1582
 
6.2%
n1582
 
6.2%
Other values (4)6328
25.0%
Common
ValueCountFrequency (%)
1582
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII26894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a3164
 
11.8%
l3164
 
11.8%
P1582
 
5.9%
e1582
 
5.9%
t1582
 
5.9%
1582
 
5.9%
C1582
 
5.9%
o1582
 
5.9%
m1582
 
5.9%
p1582
 
5.9%
Other values (5)7910
29.4%

U_Frt
Real number (ℝ≥0)

ZEROS

Distinct52
Distinct (%)3.3%
Missing4
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1816.297212
Minimum0
Maximum136000
Zeros281
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2023-04-30T22:48:11.143198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q315
95-th percentile10000
Maximum136000
Range136000
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7623.607054
Coefficient of variation (CV)4.197334558
Kurtosis179.0526546
Mean1816.297212
Median Absolute Deviation (MAD)1
Skewness11.81023856
Sum2866117
Variance58119384.52
MonotonicityNot monotonic
2023-04-30T22:48:11.320724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1599
37.9%
15322
20.4%
0281
17.8%
658
 
3.7%
1041
 
2.6%
850036
 
2.3%
500024
 
1.5%
1150023
 
1.5%
350022
 
1.4%
300016
 
1.0%
Other values (42)156
 
9.9%
ValueCountFrequency (%)
0281
17.8%
1599
37.9%
21
 
0.1%
658
 
3.7%
81
 
0.1%
1041
 
2.6%
15322
20.4%
201
 
0.1%
15001
 
0.1%
20001
 
0.1%
ValueCountFrequency (%)
1360002
0.1%
1205001
 
0.1%
950001
 
0.1%
625001
 
0.1%
535003
0.2%
500001
 
0.1%
390001
 
0.1%
250001
 
0.1%
210001
 
0.1%
180002
0.1%

U_ActShipType
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Pallet Company Transport borne by customer
1512 
Customer Transport
 
46
Pallet Company Transport borne by Pallet Company
 
16
Vendor Transport
 
8

Length

Max length48
Median length42
Mean length41.23135272
Min length16

Characters and Unicode

Total characters65228
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPallet Company Transport borne by customer
2nd rowPallet Company Transport borne by customer
3rd rowPallet Company Transport borne by customer
4th rowPallet Company Transport borne by customer
5th rowPallet Company Transport borne by customer

Common Values

ValueCountFrequency (%)
Pallet Company Transport borne by customer1512
95.6%
Customer Transport46
 
2.9%
Pallet Company Transport borne by Pallet Company16
 
1.0%
Vendor Transport8
 
0.5%

Length

2023-04-30T22:48:11.483329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:11.623959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
transport1582
17.0%
customer1558
16.8%
pallet1544
16.6%
company1544
16.6%
borne1528
16.4%
by1528
16.4%
vendor8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
7710
11.8%
r6258
 
9.6%
o6220
 
9.5%
t4684
 
7.2%
a4670
 
7.2%
n4662
 
7.1%
e4638
 
7.1%
s3140
 
4.8%
p3126
 
4.8%
m3102
 
4.8%
Other values (10)17018
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52794
80.9%
Space Separator7710
 
11.8%
Uppercase Letter4724
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r6258
11.9%
o6220
11.8%
t4684
8.9%
a4670
8.8%
n4662
8.8%
e4638
8.8%
s3140
 
5.9%
p3126
 
5.9%
m3102
 
5.9%
l3088
 
5.8%
Other values (5)9206
17.4%
Uppercase Letter
ValueCountFrequency (%)
C1590
33.7%
T1582
33.5%
P1544
32.7%
V8
 
0.2%
Space Separator
ValueCountFrequency (%)
7710
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin57518
88.2%
Common7710
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r6258
10.9%
o6220
10.8%
t4684
 
8.1%
a4670
 
8.1%
n4662
 
8.1%
e4638
 
8.1%
s3140
 
5.5%
p3126
 
5.4%
m3102
 
5.4%
l3088
 
5.4%
Other values (9)13930
24.2%
Common
ValueCountFrequency (%)
7710
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII65228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7710
11.8%
r6258
 
9.6%
o6220
 
9.5%
t4684
 
7.2%
a4670
 
7.2%
n4662
 
7.1%
e4638
 
7.1%
s3140
 
4.8%
p3126
 
4.8%
m3102
 
4.8%
Other values (10)17018
26.1%

PRODUCT CATEGORY
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Wooden Pallet
1582 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters20566
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWooden Pallet
2nd rowWooden Pallet
3rd rowWooden Pallet
4th rowWooden Pallet
5th rowWooden Pallet

Common Values

ValueCountFrequency (%)
Wooden Pallet1582
100.0%

Length

2023-04-30T22:48:11.758595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:11.879270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
wooden1582
50.0%
pallet1582
50.0%

Most occurring characters

ValueCountFrequency (%)
o3164
15.4%
e3164
15.4%
l3164
15.4%
W1582
7.7%
d1582
7.7%
n1582
7.7%
1582
7.7%
P1582
7.7%
a1582
7.7%
t1582
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15820
76.9%
Uppercase Letter3164
 
15.4%
Space Separator1582
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o3164
20.0%
e3164
20.0%
l3164
20.0%
d1582
10.0%
n1582
10.0%
a1582
10.0%
t1582
10.0%
Uppercase Letter
ValueCountFrequency (%)
W1582
50.0%
P1582
50.0%
Space Separator
ValueCountFrequency (%)
1582
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18984
92.3%
Common1582
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o3164
16.7%
e3164
16.7%
l3164
16.7%
W1582
8.3%
d1582
8.3%
n1582
8.3%
P1582
8.3%
a1582
8.3%
t1582
8.3%
Common
ValueCountFrequency (%)
1582
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20566
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o3164
15.4%
e3164
15.4%
l3164
15.4%
W1582
7.7%
d1582
7.7%
n1582
7.7%
1582
7.7%
P1582
7.7%
a1582
7.7%
t1582
7.7%

ItemCode
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
A010000023
1182 
A010000038
121 
A010000003
121 
A010000104
 
111
A010000054
 
23
Other values (6)
 
24

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters15820
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowA010000023
2nd rowA010000023
3rd rowA010000054
4th rowA010000023
5th rowA010000023

Common Values

ValueCountFrequency (%)
A0100000231182
74.7%
A010000038121
 
7.6%
A010000003121
 
7.6%
A010000104111
 
7.0%
A01000005423
 
1.5%
A01000001714
 
0.9%
A0100000064
 
0.3%
A0100000243
 
0.2%
A0100000071
 
0.1%
A0300000171
 
0.1%

Length

2023-04-30T22:48:11.986967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a0100000231182
74.7%
a010000038121
 
7.6%
a010000003121
 
7.6%
a010000104111
 
7.0%
a01000005423
 
1.5%
a01000001714
 
0.9%
a0100000064
 
0.3%
a0100000243
 
0.2%
a0100000071
 
0.1%
a0300000171
 
0.1%

Most occurring characters

ValueCountFrequency (%)
09618
60.8%
11707
 
10.8%
A1582
 
10.0%
31425
 
9.0%
21185
 
7.5%
4137
 
0.9%
8121
 
0.8%
524
 
0.2%
717
 
0.1%
64
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14238
90.0%
Uppercase Letter1582
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09618
67.6%
11707
 
12.0%
31425
 
10.0%
21185
 
8.3%
4137
 
1.0%
8121
 
0.8%
524
 
0.2%
717
 
0.1%
64
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
A1582
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common14238
90.0%
Latin1582
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09618
67.6%
11707
 
12.0%
31425
 
10.0%
21185
 
8.3%
4137
 
1.0%
8121
 
0.8%
524
 
0.2%
717
 
0.1%
64
 
< 0.1%
Latin
ValueCountFrequency (%)
A1582
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09618
60.8%
11707
 
10.8%
A1582
 
10.0%
31425
 
9.0%
21185
 
7.5%
4137
 
0.9%
8121
 
0.8%
524
 
0.2%
717
 
0.1%
64
 
< 0.1%

QUANTITY
Real number (ℝ≥0)

HIGH CORRELATION

Distinct203
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.8482933
Minimum1
Maximum540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2023-04-30T22:48:12.125616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile100
Q1200
median300
Q3400
95-th percentile500
Maximum540
Range539
Interquartile range (IQR)200

Descriptive statistics

Standard deviation127.7890161
Coefficient of variation (CV)0.4247623102
Kurtosis-0.8320066861
Mean300.8482933
Median Absolute Deviation (MAD)100
Skewness-0.007435640738
Sum475942
Variance16330.03263
MonotonicityNot monotonic
2023-04-30T22:48:12.295158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300341
21.6%
200153
 
9.7%
40078
 
4.9%
23073
 
4.6%
48666
 
4.2%
50065
 
4.1%
35058
 
3.7%
18056
 
3.5%
15054
 
3.4%
45046
 
2.9%
Other values (193)592
37.4%
ValueCountFrequency (%)
15
0.3%
21
 
0.1%
51
 
0.1%
81
 
0.1%
101
 
0.1%
122
 
0.1%
141
 
0.1%
202
 
0.1%
251
 
0.1%
271
 
0.1%
ValueCountFrequency (%)
5401
 
0.1%
5361
 
0.1%
5281
 
0.1%
52625
 
1.6%
5202
 
0.1%
5121
 
0.1%
5071
 
0.1%
5062
 
0.1%
5012
 
0.1%
50065
4.1%

UNIT
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Nos
1582 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4746
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNos
2nd rowNos
3rd rowNos
4th rowNos
5th rowNos

Common Values

ValueCountFrequency (%)
Nos1582
100.0%

Length

2023-04-30T22:48:12.442766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:12.566394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
nos1582
100.0%

Most occurring characters

ValueCountFrequency (%)
N1582
33.3%
o1582
33.3%
s1582
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3164
66.7%
Uppercase Letter1582
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1582
50.0%
s1582
50.0%
Uppercase Letter
ValueCountFrequency (%)
N1582
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4746
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N1582
33.3%
o1582
33.3%
s1582
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4746
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N1582
33.3%
o1582
33.3%
s1582
33.3%

RATE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2053.465234
Minimum1200
Maximum3375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2023-04-30T22:48:12.659191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1500
Q11950
median1950
Q31950
95-th percentile2890
Maximum3375
Range2175
Interquartile range (IQR)0

Descriptive statistics

Standard deviation376.8625695
Coefficient of variation (CV)0.1835251765
Kurtosis1.000093339
Mean2053.465234
Median Absolute Deviation (MAD)0
Skewness1.213583074
Sum3248582
Variance142025.3963
MonotonicityNot monotonic
2023-04-30T22:48:12.761896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
19501182
74.7%
1500125
 
7.9%
2890121
 
7.6%
2850111
 
7.0%
259923
 
1.5%
125014
 
0.9%
21803
 
0.2%
17501
 
0.1%
33751
 
0.1%
12001
 
0.1%
ValueCountFrequency (%)
12001
 
0.1%
125014
 
0.9%
1500125
 
7.9%
17501
 
0.1%
19501182
74.7%
21803
 
0.2%
259923
 
1.5%
2850111
 
7.0%
2890121
 
7.6%
33751
 
0.1%
ValueCountFrequency (%)
33751
 
0.1%
2890121
 
7.6%
2850111
 
7.0%
259923
 
1.5%
21803
 
0.2%
19501182
74.7%
17501
 
0.1%
1500125
 
7.9%
125014
 
0.9%
12001
 
0.1%

SO ID
Categorical

HIGH CARDINALITY

Distinct424
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
2020/6995
 
45
2020/2306
 
34
2021/1802
 
33
2021/7167
 
26
2021/1433
 
26
Other values (419)
1418 

Length

Max length10
Median length9
Mean length8.994310999
Min length8

Characters and Unicode

Total characters14229
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)12.3%

Sample

1st row2019/493
2nd row2019/493
3rd row2019/1133
4th row2019/1309
5th row2019/1309

Common Values

ValueCountFrequency (%)
2020/699545
 
2.8%
2020/230634
 
2.1%
2021/180233
 
2.1%
2021/716726
 
1.6%
2021/143326
 
1.6%
2021/315925
 
1.6%
2021/282424
 
1.5%
2020/352323
 
1.5%
2021/217023
 
1.5%
2021/180423
 
1.5%
Other values (414)1300
82.2%

Length

2023-04-30T22:48:12.892568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020/699545
 
2.8%
2020/230634
 
2.1%
2021/180233
 
2.1%
2021/716726
 
1.6%
2021/143326
 
1.6%
2021/315925
 
1.6%
2021/282424
 
1.5%
2020/352323
 
1.5%
2021/217023
 
1.5%
2021/180423
 
1.5%
Other values (414)1300
82.2%

Most occurring characters

ValueCountFrequency (%)
24287
30.1%
02772
19.5%
/1582
 
11.1%
11298
 
9.1%
3918
 
6.5%
9786
 
5.5%
4596
 
4.2%
6538
 
3.8%
5536
 
3.8%
8486
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12647
88.9%
Other Punctuation1582
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
24287
33.9%
02772
21.9%
11298
 
10.3%
3918
 
7.3%
9786
 
6.2%
4596
 
4.7%
6538
 
4.3%
5536
 
4.2%
8486
 
3.8%
7430
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/1582
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common14229
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
24287
30.1%
02772
19.5%
/1582
 
11.1%
11298
 
9.1%
3918
 
6.5%
9786
 
5.5%
4596
 
4.2%
6538
 
3.8%
5536
 
3.8%
8486
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII14229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24287
30.1%
02772
19.5%
/1582
 
11.1%
11298
 
9.1%
3918
 
6.5%
9786
 
5.5%
4596
 
4.2%
6538
 
3.8%
5536
 
3.8%
8486
 
3.4%
Distinct217
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Minimum2019-12-05 00:00:00
Maximum2022-11-17 00:00:00
2023-04-30T22:48:13.042164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:13.205683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct229
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Minimum2019-12-05 00:00:00
Maximum2022-11-21 00:00:00
2023-04-30T22:48:13.616071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:13.785624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

U_DocStatus
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
C
1579 
O
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1582
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C1579
99.8%
O3
 
0.2%

Length

2023-04-30T22:48:13.935181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:14.058893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
c1579
99.8%
o3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C1579
99.8%
O3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1582
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C1579
99.8%
O3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin1582
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C1579
99.8%
O3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C1579
99.8%
O3
 
0.2%

NumAtCard
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1582
Missing (%)100.0%
Memory size12.5 KiB

U_SOTYPE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Rent
1575 
Trial
 
7

Length

Max length5
Median length4
Mean length4.004424779
Min length4

Characters and Unicode

Total characters6335
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRent
2nd rowRent
3rd rowRent
4th rowRent
5th rowRent

Common Values

ValueCountFrequency (%)
Rent1575
99.6%
Trial7
 
0.4%

Length

2023-04-30T22:48:14.172578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:14.299232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
rent1575
99.6%
trial7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
R1575
24.9%
e1575
24.9%
n1575
24.9%
t1575
24.9%
T7
 
0.1%
r7
 
0.1%
i7
 
0.1%
a7
 
0.1%
l7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4753
75.0%
Uppercase Letter1582
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1575
33.1%
n1575
33.1%
t1575
33.1%
r7
 
0.1%
i7
 
0.1%
a7
 
0.1%
l7
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
R1575
99.6%
T7
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin6335
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R1575
24.9%
e1575
24.9%
n1575
24.9%
t1575
24.9%
T7
 
0.1%
r7
 
0.1%
i7
 
0.1%
a7
 
0.1%
l7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII6335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R1575
24.9%
e1575
24.9%
n1575
24.9%
t1575
24.9%
T7
 
0.1%
r7
 
0.1%
i7
 
0.1%
a7
 
0.1%
l7
 
0.1%

BP CATEGORY
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Ecommerce
1575 
3PL
 
7

Length

Max length9
Median length9
Mean length8.973451327
Min length3

Characters and Unicode

Total characters14196
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEcommerce
2nd rowEcommerce
3rd rowEcommerce
4th rowEcommerce
5th rowEcommerce

Common Values

ValueCountFrequency (%)
Ecommerce1575
99.6%
3PL7
 
0.4%

Length

2023-04-30T22:48:14.414923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:14.549577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ecommerce1575
99.6%
3pl7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
c3150
22.2%
m3150
22.2%
e3150
22.2%
E1575
11.1%
o1575
11.1%
r1575
11.1%
37
 
< 0.1%
P7
 
< 0.1%
L7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12600
88.8%
Uppercase Letter1589
 
11.2%
Decimal Number7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c3150
25.0%
m3150
25.0%
e3150
25.0%
o1575
12.5%
r1575
12.5%
Uppercase Letter
ValueCountFrequency (%)
E1575
99.1%
P7
 
0.4%
L7
 
0.4%
Decimal Number
ValueCountFrequency (%)
37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14189
> 99.9%
Common7
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
c3150
22.2%
m3150
22.2%
e3150
22.2%
E1575
11.1%
o1575
11.1%
r1575
11.1%
P7
 
< 0.1%
L7
 
< 0.1%
Common
ValueCountFrequency (%)
37
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII14196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c3150
22.2%
m3150
22.2%
e3150
22.2%
E1575
11.1%
o1575
11.1%
r1575
11.1%
37
 
< 0.1%
P7
 
< 0.1%
L7
 
< 0.1%

Document Type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Allot
1582 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters7910
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAllot
2nd rowAllot
3rd rowAllot
4th rowAllot
5th rowAllot

Common Values

ValueCountFrequency (%)
Allot1582
100.0%

Length

2023-04-30T22:48:14.669257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:14.795920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
allot1582
100.0%

Most occurring characters

ValueCountFrequency (%)
l3164
40.0%
A1582
20.0%
o1582
20.0%
t1582
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6328
80.0%
Uppercase Letter1582
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l3164
50.0%
o1582
25.0%
t1582
25.0%
Uppercase Letter
ValueCountFrequency (%)
A1582
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7910
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l3164
40.0%
A1582
20.0%
o1582
20.0%
t1582
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7910
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l3164
40.0%
A1582
20.0%
o1582
20.0%
t1582
20.0%

Vehicle Type
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
32ft SXL
573 
32ft MXL
271 
40ft Open Trailer
238 
19ft Vehicle
194 
20ft Vehicle
142 
Other values (11)
164 

Length

Max length22
Median length8
Mean length11.1795196
Min length7

Characters and Unicode

Total characters17686
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row19ft Vehicle
2nd row19ft Part Load
3rd row32ft SXL
4th row19ft Vehicle
5th row32ft SXL

Common Values

ValueCountFrequency (%)
32ft SXL573
36.2%
32ft MXL271
17.1%
40ft Open Trailer238
15.0%
19ft Vehicle194
 
12.3%
20ft Vehicle142
 
9.0%
40ft Container Trailer92
 
5.8%
22ft Vehicle20
 
1.3%
17ft Vehicle17
 
1.1%
19ft Part Load13
 
0.8%
Pick Up5
 
0.3%
Other values (6)17
 
1.1%

Length

2023-04-30T22:48:14.908622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
32ft844
24.1%
sxl573
16.3%
vehicle380
10.8%
40ft330
 
9.4%
trailer330
 
9.4%
mxl271
 
7.7%
open238
 
6.8%
19ft207
 
5.9%
20ft143
 
4.1%
container92
 
2.6%
Other values (13)101
 
2.9%

Most occurring characters

ValueCountFrequency (%)
1927
 
10.9%
t1683
 
9.5%
f1563
 
8.8%
e1425
 
8.1%
21028
 
5.8%
L858
 
4.9%
3846
 
4.8%
X844
 
4.8%
i807
 
4.6%
r766
 
4.3%
Other values (24)5939
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8982
50.8%
Uppercase Letter3629
20.5%
Decimal Number3148
 
17.8%
Space Separator1927
 
10.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t1683
18.7%
f1563
17.4%
e1425
15.9%
i807
9.0%
r766
8.5%
l710
7.9%
a468
 
5.2%
n422
 
4.7%
c390
 
4.3%
h380
 
4.2%
Other values (4)368
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
L858
23.6%
X844
23.3%
S573
15.8%
V380
10.5%
T339
 
9.3%
M271
 
7.5%
O238
 
6.6%
C92
 
2.5%
P19
 
0.5%
U5
 
0.1%
Other values (2)10
 
0.3%
Decimal Number
ValueCountFrequency (%)
21028
32.7%
3846
26.9%
0477
15.2%
4341
 
10.8%
1228
 
7.2%
9207
 
6.6%
721
 
0.7%
Space Separator
ValueCountFrequency (%)
1927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12611
71.3%
Common5075
28.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t1683
13.3%
f1563
12.4%
e1425
11.3%
L858
 
6.8%
X844
 
6.7%
i807
 
6.4%
r766
 
6.1%
l710
 
5.6%
S573
 
4.5%
a468
 
3.7%
Other values (16)2914
23.1%
Common
ValueCountFrequency (%)
1927
38.0%
21028
20.3%
3846
16.7%
0477
 
9.4%
4341
 
6.7%
1228
 
4.5%
9207
 
4.1%
721
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII17686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1927
 
10.9%
t1683
 
9.5%
f1563
 
8.8%
e1425
 
8.1%
21028
 
5.8%
L858
 
4.9%
3846
 
4.8%
X844
 
4.8%
i807
 
4.6%
r766
 
4.3%
Other values (24)5939
33.6%

Direct Dispatch
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
False
880 
True
702 
ValueCountFrequency (%)
False880
55.6%
True702
44.4%
2023-04-30T22:48:15.040258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Comments
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1549
Missing (%)97.9%
Memory size12.5 KiB

U_GRNNO
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct668
Distinct (%)92.6%
Missing861
Missing (%)54.4%
Infinite0
Infinite (%)0.0%
Mean1801.85853
Minimum1
Maximum12282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2023-04-30T22:48:15.168885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile656
Q11138
median1444
Q32102
95-th percentile4541
Maximum12282
Range12281
Interquartile range (IQR)964

Descriptive statistics

Standard deviation1196.054914
Coefficient of variation (CV)0.6637895786
Kurtosis16.32012094
Mean1801.85853
Median Absolute Deviation (MAD)409
Skewness2.909106213
Sum1299140
Variance1430547.358
MonotonicityNot monotonic
2023-04-30T22:48:15.342462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12353
 
0.2%
15842
 
0.1%
11032
 
0.1%
12252
 
0.1%
16552
 
0.1%
16862
 
0.1%
27422
 
0.1%
11682
 
0.1%
12
 
0.1%
11392
 
0.1%
Other values (658)700
44.2%
(Missing)861
54.4%
ValueCountFrequency (%)
12
0.1%
41
0.1%
111
0.1%
321
0.1%
541
0.1%
631
0.1%
1011
0.1%
1421
0.1%
1611
0.1%
1632
0.1%
ValueCountFrequency (%)
122821
0.1%
121751
0.1%
51111
0.1%
50541
0.1%
50531
0.1%
50281
0.1%
50261
0.1%
50151
0.1%
50111
0.1%
49801
0.1%

Loading/Unloading
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct6
Distinct (%)0.5%
Missing487
Missing (%)30.8%
Infinite0
Infinite (%)0.0%
Mean26.66940639
Minimum0
Maximum1800
Zeros1065
Zeros (%)67.3%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2023-04-30T22:48:15.478058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1800
Range1800
Interquartile range (IQR)0

Descriptive statistics

Standard deviation171.7241749
Coefficient of variation (CV)6.438995019
Kurtosis43.84584797
Mean26.66940639
Median Absolute Deviation (MAD)0
Skewness6.597955858
Sum29203
Variance29489.19225
MonotonicityNot monotonic
2023-04-30T22:48:15.592749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
01065
67.3%
120016
 
1.0%
8009
 
0.6%
13
 
0.2%
18001
 
0.1%
10001
 
0.1%
(Missing)487
30.8%
ValueCountFrequency (%)
01065
67.3%
13
 
0.2%
8009
 
0.6%
10001
 
0.1%
120016
 
1.0%
18001
 
0.1%
ValueCountFrequency (%)
18001
 
0.1%
120016
 
1.0%
10001
 
0.1%
8009
 
0.6%
13
 
0.2%
01065
67.3%

Detention
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)0.2%
Missing1138
Missing (%)71.9%
Memory size12.5 KiB
0.0
444 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1332
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0444
 
28.1%
(Missing)1138
71.9%

Length

2023-04-30T22:48:15.724397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:15.844111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0444
100.0%

Most occurring characters

ValueCountFrequency (%)
0888
66.7%
.444
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number888
66.7%
Other Punctuation444
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0888
100.0%
Other Punctuation
ValueCountFrequency (%)
.444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0888
66.7%
.444
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0888
66.7%
.444
33.3%

KITITEM
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1582
Missing (%)100.0%
Memory size12.5 KiB

U_AssetClass
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Wooden Pallet
1581 
Wooden Pallet - SW
 
1

Length

Max length18
Median length13
Mean length13.00316056
Min length13

Characters and Unicode

Total characters20571
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowWooden Pallet
2nd rowWooden Pallet
3rd rowWooden Pallet
4th rowWooden Pallet
5th rowWooden Pallet

Common Values

ValueCountFrequency (%)
Wooden Pallet1581
99.9%
Wooden Pallet - SW1
 
0.1%

Length

2023-04-30T22:48:15.954812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-30T22:48:16.083477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
wooden1582
50.0%
pallet1582
50.0%
1
 
< 0.1%
sw1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o3164
15.4%
e3164
15.4%
l3164
15.4%
1584
7.7%
W1583
7.7%
d1582
7.7%
n1582
7.7%
P1582
7.7%
a1582
7.7%
t1582
7.7%
Other values (2)2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15820
76.9%
Uppercase Letter3166
 
15.4%
Space Separator1584
 
7.7%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o3164
20.0%
e3164
20.0%
l3164
20.0%
d1582
10.0%
n1582
10.0%
a1582
10.0%
t1582
10.0%
Uppercase Letter
ValueCountFrequency (%)
W1583
50.0%
P1582
50.0%
S1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1584
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18986
92.3%
Common1585
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o3164
16.7%
e3164
16.7%
l3164
16.7%
W1583
8.3%
d1582
8.3%
n1582
8.3%
P1582
8.3%
a1582
8.3%
t1582
8.3%
S1
 
< 0.1%
Common
ValueCountFrequency (%)
1584
99.9%
-1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII20571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o3164
15.4%
e3164
15.4%
l3164
15.4%
1584
7.7%
W1583
7.7%
d1582
7.7%
n1582
7.7%
P1582
7.7%
a1582
7.7%
t1582
7.7%
Other values (2)2
 
< 0.1%

Interactions

2023-04-30T22:48:03.322762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:56.568344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:57.884823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:58.974909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:00.064995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:01.192491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:02.173831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:03.537186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:56.730907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:58.047390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:59.134483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:00.209609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:01.334076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:02.332406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:03.745631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:56.898460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:58.232892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:59.305029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:00.356217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:01.474724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:02.490982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:04.000946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:57.083963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:58.396457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:59.469588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:00.507811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:01.612357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:02.643575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:04.156533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:57.254508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:58.542066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:59.609213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:00.643476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:01.743980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:02.789251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:04.302140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:57.497860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:58.672717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:59.737870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:00.763130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:01.879617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:02.929811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:04.448124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:57.730236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:58.826306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:47:59.897470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:00.908740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:02.022262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-04-30T22:48:03.132270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-04-30T22:48:16.199131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-04-30T22:48:16.433529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-04-30T22:48:16.676855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-04-30T22:48:16.920243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-04-30T22:48:17.271266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-04-30T22:48:04.845032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-30T22:48:06.037844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-30T22:48:06.342031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-04-30T22:48:06.551501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

POSTING DATEEFFECTIVE DATECREATE DATECustomer/Vendor CodeCustomer/Vendor NameLOBRegionBP TYPECitySTATEFrom WhsCodeFrom WhsNameTo whsCodeTO WhsNameModel TYPETransfer TypeU_FrtU_ActShipTypePRODUCT CATEGORYItemCodeQUANTITYUNITRATESO IDSO Creation DateSO Due DateU_DocStatusNumAtCardU_SOTYPEBP CATEGORYDocument TypeVehicle TypeDirect DispatchCommentsU_GRNNOLoading/UnloadingDetentionKITITEMU_AssetClass
02019-12-062019-12-062019-12-0610002355Cart Services Pvt. Ltd. _ Howrah _Uluberia ESR WarehousingFMCGEastRENTALHowrahWest BengalWB01Pallet Company India Kolkata10002355Cart Services Pvt. Ltd. - Howrah (Uluberia, ESR Warehousing)AllotPallet CompanyOUT8500.0Pallet Company Transport borne by customerWooden PalletA010000023200Nos19502019/4932019-12-052019-12-05CNaNRentEcommerceAllot19ft VehicleN191900083NaN800.0NaNNaNWooden Pallet
12019-12-062019-12-062019-12-0610002355Cart Services Pvt. Ltd. _ Howrah _Uluberia ESR WarehousingFMCGEastRENTALHowrahWest BengalWB01Pallet Company India Kolkata10002355Cart Services Pvt. Ltd. - Howrah (Uluberia, ESR Warehousing)AllotPallet CompanyOUT8500.0Pallet Company Transport borne by customerWooden PalletA010000023200Nos19502019/4932019-12-052019-12-05CNaNRentEcommerceAllot19ft Part LoadN191900082NaN800.0NaNNaNWooden Pallet
22019-12-202019-12-202019-12-2010003155Cart Services Pvt Ltd _ Gurgoan _Embassy IndustrialFMCGNorthRENTALGurgaonHaryanaHY01Pallet Company India Pataudi10003155Cart Services Pvt. Ltd. - Gurgoan (Embassy Industrial)AllotPallet CompanyOUT4500.0Pallet Company Transport borne by customerWooden PalletA010000054200Nos25992019/11332019-12-202019-12-27CNaNRentEcommerceAllot32ft SXLN61900469NaN0.0NaNNaNWooden Pallet
32019-12-262019-12-262019-12-2610001155Cart Services Pvt. Ltd. _ Gurgoan _ TauruFMCGNorthRENTALGurgaonHaryanaHY01Pallet Company India Pataudi10001155Cart Services Pvt. Ltd. - Gurgoan ( Tauru)AllotPallet CompanyOUT3500.0Pallet Company Transport borne by customerWooden PalletA010000023180Nos19502019/13092019-12-262020-01-02CNaNRentEcommerceAllot19ft VehicleN61900477NaN0.0NaNNaNWooden Pallet
42019-12-262019-12-262019-12-2610001155Cart Services Pvt. Ltd. _ Gurgoan _ TauruFMCGNorthRENTALGurgaonHaryanaHY01Pallet Company India Pataudi10001155Cart Services Pvt. Ltd. - Gurgoan ( Tauru)AllotPallet CompanyOUT4500.0Pallet Company Transport borne by customerWooden PalletA010000023326Nos19502019/13092019-12-262020-01-02CNaNRentEcommerceAllot32ft SXLN61900478NaN0.0NaNNaNWooden Pallet
52019-12-282019-12-282019-12-2810001155Cart Services Pvt. Ltd. _ Gurgoan _ TauruFMCGNorthRENTALGurgaonHaryanaHY01Pallet Company India Pataudi10001155Cart Services Pvt. Ltd. - Gurgoan ( Tauru)AllotPallet CompanyOUT0.0Pallet Company Transport borne by customerWooden PalletA010000023494Nos19502019/13092019-12-262020-01-02CNaNRentEcommerceAllot32ft MXLN61900564NaN0.0NaNNaNWooden Pallet
62020-02-072020-02-072020-02-0710001497Cart Services Pvt. Ltd. _ Jhajjar _Flex Kulana FCFMCGNorthRENTALJhajjarHaryanaKA02Pallet Company India Bangalore-Hobli10001497Cart Services Pvt. Ltd. - Jhajjar (Flex Kulana FC)AllotPallet CompanyOUT53500.0Pallet Company Transport borne by customerWooden PalletA010000038230Nos28902019/32042020-02-042020-02-10CNaNRentEcommerceAllot32ft MXLN291900912NaN0.0NaNNaNWooden Pallet
72020-02-072020-02-072020-02-0710001497Cart Services Pvt. Ltd. _ Jhajjar _Flex Kulana FCFMCGNorthRENTALJhajjarHaryanaKA02Pallet Company India Bangalore-Hobli10001497Cart Services Pvt. Ltd. - Jhajjar (Flex Kulana FC)AllotPallet CompanyOUT53500.0Pallet Company Transport borne by customerWooden PalletA010000038230Nos28902019/32042020-02-042020-02-10CNaNRentEcommerceAllot32ft MXLN291900914NaN0.0NaNNaNWooden Pallet
82020-02-082020-02-082020-02-0810001725Cart Services Pvt. Ltd. _ Bhiwandi _WH B6and B8FMCGWestRENTALBhiwandiMaharashtraMH03Pallet Company India Bhiwandi10001725Cart Services Pvt. Ltd. - Bhiwandi (WH B6&B8)AllotPallet CompanyOUT4000.0Pallet Company Transport borne by customerWooden PalletA010000023300Nos19502019/34502020-02-082020-02-09CNaNRentEcommerceAllot32ft SXLN271902158NaNNaNNaNNaNWooden Pallet
92020-02-172020-02-172020-02-1710001497Cart Services Pvt. Ltd. _ Jhajjar _Flex Kulana FCFMCGNorthRENTALJhajjarHaryanaHY01Pallet Company India Pataudi10001497Cart Services Pvt. Ltd. - Jhajjar (Flex Kulana FC)AllotPallet CompanyOUT62500.0Pallet Company Transport borne by customerWooden PalletA010000038230Nos28902019/32042020-02-042020-02-10CNaNRentEcommerceAllot32ft MXLY61901640NaN0.0NaNNaNWooden Pallet

Last rows

POSTING DATEEFFECTIVE DATECREATE DATECustomer/Vendor CodeCustomer/Vendor NameLOBRegionBP TYPECitySTATEFrom WhsCodeFrom WhsNameTo whsCodeTO WhsNameModel TYPETransfer TypeU_FrtU_ActShipTypePRODUCT CATEGORYItemCodeQUANTITYUNITRATESO IDSO Creation DateSO Due DateU_DocStatusNumAtCardU_SOTYPEBP CATEGORYDocument TypeVehicle TypeDirect DispatchCommentsU_GRNNOLoading/UnloadingDetentionKITITEMU_AssetClass
15722022-11-142022-11-142022-11-1410001216Cart Services Pvt. Ltd. _ Bangalore _DasanapuraFMCGSouthRENTALBangaloreKarnatakaTL02Pallet Company India Hyderabad10001216Cart Services Pvt. Ltd. - Bangalore (Dasanapura)AllotPallet CompanyOUT1.0Pallet Company Transport borne by customerWooden PalletA010000017200Nos12502022/51992022-10-292022-10-31CNaNRentEcommerceAllot32ft SXLNNaNNaNNaNNaNNaNWooden Pallet
15732022-11-172022-11-172022-11-1710006335Cart Services Private Limited_Thiruvallur_OrakkaduFMCGSouthRENTALTiruvallurTamil NaduTN02Pallet Company India Chennai10006335Cart Services Private Limited_Thiruvallur_OrakkaduAllotPallet CompanyOUT0.0Pallet Company Transport borne by customerWooden PalletA010000017170Nos12502022/53032022-11-022022-11-03CNaNRentEcommerceAllot20ft VehicleNNaNNaN0.00.0NaNWooden Pallet
15742022-11-182022-11-182022-11-1810008314Cart Services Private Limited_ Unnao_HasanganjFMCGNorthRENTALUnnaoUttar PradeshMH01Pallet Company India HO10008314Cart Services Private Limited_ Unnao_HasanganjAllotPallet CompanyOUT15.0Pallet Company Transport borne by customerWooden PalletA010000023450Nos19502022/56662022-11-172022-11-18CNaNRent3PLAllot32ft MXLYNaN2731.00.00.0NaNWooden Pallet
15752022-11-182022-11-182022-11-1810001216Cart Services Pvt. Ltd. _ Bangalore _DasanapuraFMCGSouthRENTALBangaloreKarnatakaTN02Pallet Company India Chennai10001216Cart Services Pvt. Ltd. - Bangalore (Dasanapura)AllotPallet CompanyOUT0.0Pallet Company Transport borne by customerWooden PalletA010000017100Nos12502022/51992022-10-292022-10-31CNaNRentEcommerceAllot17ft VehicleNNaNNaN0.00.0NaNWooden Pallet
15762022-11-192022-11-192022-11-1910006335Cart Services Private Limited_Thiruvallur_OrakkaduFMCGSouthRENTALTiruvallurTamil NaduTN02Pallet Company India Chennai10006335Cart Services Private Limited_Thiruvallur_OrakkaduAllotPallet CompanyOUT0.0Pallet Company Transport borne by customerWooden PalletA010000017180Nos12502022/53032022-11-022022-11-03CNaNRentEcommerceAllot20ft VehicleNNaNNaN0.00.0NaNWooden Pallet
15772022-11-192022-11-192022-11-1910008314Cart Services Private Limited_ Unnao_HasanganjFMCGNorthRENTALUnnaoUttar PradeshMH01Pallet Company India HO10008314Cart Services Private Limited_ Unnao_HasanganjAllotPallet CompanyOUT15.0Pallet Company Transport borne by customerWooden PalletA010000023500Nos19502022/56662022-11-172022-11-18CNaNRent3PLAllot32ft MXLYNaN2742.00.00.0NaNWooden Pallet
15782022-11-212022-11-212022-11-2110008314Cart Services Private Limited_ Unnao_HasanganjFMCGNorthRENTALUnnaoUttar PradeshMH01Pallet Company India HO10008314Cart Services Private Limited_ Unnao_HasanganjAllotPallet CompanyOUT15.0Pallet Company Transport borne by customerWooden PalletA010000023490Nos19502022/56662022-11-172022-11-18ONaNRent3PLAllot32ft MXLYNaN2758.0NaNNaNNaNWooden Pallet
15792022-11-222022-11-222022-11-2210006814Cart Services Pvt. Ltd. _ Bhiwandi_CFFMCGWestRENTALThaneMaharashtraMH07Pallet Company India Bhiwandi10006814Cart Services Pvt. Ltd. _ Bhiwandi_CFAllotPallet CompanyOUT6.0Pallet Company Transport borne by customerWooden PalletA010000017200Nos12502022/55952022-11-142022-11-17CNaNRentEcommerceAllot20ft VehicleNNaNNaNNaNNaNNaNWooden Pallet
15802022-11-262022-11-262022-11-2610006524Cart Services Pvt. Ltd. _ Hyderabad _PudurFMCGSouthRENTALHyderabadTelanganaMH07Pallet Company India Bhiwandi10006524Cart Services Pvt. Ltd. _ Hyderabad _PudurAllotPallet CompanyOUT1.0Pallet Company Transport borne by customerWooden PalletA010000017200Nos12502022/56502022-11-172022-11-21ONaNRentEcommerceAllot32ft SXLNNaNNaNNaNNaNNaNWooden Pallet
15812022-11-262022-11-262022-11-2610008338Cart Services Private Limited_Hyderabad_Medchal_PudurFMCGSouthRENTALHyderabadTelanganaMH07Pallet Company India Bhiwandi10008338Cart Services Private Limited_Hyderabad_Medchal_PudurAllotPallet CompanyOUT1.0Pallet Company Transport borne by customerWooden PalletA010000017200Nos12502022/56122022-11-152022-11-17ONaNRentEcommerceAllot32ft SXLNNaNNaNNaNNaNNaNWooden Pallet

Duplicate rows

Most frequently occurring

POSTING DATEEFFECTIVE DATECREATE DATECustomer/Vendor CodeCustomer/Vendor NameLOBRegionBP TYPECitySTATEFrom WhsCodeFrom WhsNameTo whsCodeTO WhsNameModel TYPETransfer TypeU_FrtU_ActShipTypePRODUCT CATEGORYItemCodeQUANTITYUNITRATESO IDSO Creation DateSO Due DateU_DocStatusU_SOTYPEBP CATEGORYDocument TypeVehicle TypeDirect DispatchU_GRNNOLoading/UnloadingDetentionU_AssetClass# duplicates
722021-12-292021-12-292021-12-2910007199Cart Services Private Limited_Dharwad FCFMCGSouthRENTALDharwadKarnatakaKA02Pallet Company India Bangalore-Hobli10007199Cart Services Private Limited_Dharwad FCAllotPallet CompanyOUT1.0Pallet Company Transport borne by customerWooden PalletA010000023300Nos19502021/71672021-12-232021-12-24CRentEcommerceAllot32ft SXLNNaNNaNNaNWooden Pallet6
192020-09-052020-09-052020-09-0510003155Cart Services Pvt Ltd _ Gurgoan _Embassy IndustrialFMCGNorthRENTALGurgaonHaryanaMH03Pallet Company India Bhiwandi10003155Cart Services Pvt. Ltd. - Gurgoan (Embassy Industrial)AllotPallet CompanyOUT6.0Customer TransportWooden PalletA010000038107Nos28902020/33682020-09-052020-09-07CRentEcommerceAllot32ft SXLNNaNNaNNaNWooden Pallet5
402021-01-042021-01-042021-01-0410006243Cart Services Pvt. Ltd. _ Coimbatore_ SelakarichalFMCGSouthRENTALCoimbatoreTamil NaduKA02Pallet Company India Bangalore-Hobli10006243Cart Services Pvt. Ltd. _ Coimbatore_ SelakarichalAllotPallet CompanyOUT15.0Pallet Company Transport borne by customerWooden PalletA010000023350Nos19502020/69952020-12-082021-01-02CRentEcommerceAllot32ft MXLNNaN0.0NaNWooden Pallet5
692021-12-252021-12-252021-12-2510007199Cart Services Private Limited_Dharwad FCFMCGSouthRENTALDharwadKarnatakaKA02Pallet Company India Bangalore-Hobli10007199Cart Services Private Limited_Dharwad FCAllotPallet CompanyOUT1.0Pallet Company Transport borne by customerWooden PalletA010000023322Nos19502021/71672021-12-232021-12-24CRentEcommerceAllot32ft SXLNNaNNaNNaNWooden Pallet5
172020-09-052020-09-052020-09-0510001497Cart Services Pvt. Ltd. _ Jhajjar _Flex Kulana FCFMCGNorthRENTALJhajjarHaryanaMH03Pallet Company India Bhiwandi10001497Cart Services Pvt. Ltd. - Jhajjar (Flex Kulana FC)AllotPallet CompanyOUT6.0Customer TransportWooden PalletA010000038105Nos28902020/33342020-09-042020-09-05CRentEcommerceAllot32ft SXLNNaNNaNNaNWooden Pallet4
702021-12-272021-12-272021-12-2710007199Cart Services Private Limited_Dharwad FCFMCGSouthRENTALDharwadKarnatakaKA02Pallet Company India Bangalore-Hobli10007199Cart Services Private Limited_Dharwad FCAllotPallet CompanyOUT1.0Pallet Company Transport borne by customerWooden PalletA010000023322Nos19502021/71672021-12-232021-12-24CRentEcommerceAllot32ft SXLNNaNNaNNaNWooden Pallet4
712021-12-282021-12-282021-12-2810007199Cart Services Private Limited_Dharwad FCFMCGSouthRENTALDharwadKarnatakaKA02Pallet Company India Bangalore-Hobli10007199Cart Services Private Limited_Dharwad FCAllotPallet CompanyOUT1.0Pallet Company Transport borne by customerWooden PalletA010000023300Nos19502021/71672021-12-232021-12-24CRentEcommerceAllot32ft SXLNNaNNaNNaNWooden Pallet4
732021-12-302021-12-302021-12-3010007199Cart Services Private Limited_Dharwad FCFMCGSouthRENTALDharwadKarnatakaKA02Pallet Company India Bangalore-Hobli10007199Cart Services Private Limited_Dharwad FCAllotPallet CompanyOUT1.0Pallet Company Transport borne by customerWooden PalletA010000023300Nos19502021/71672021-12-232021-12-24CRentEcommerceAllot32ft SXLNNaNNaNNaNWooden Pallet4
12020-07-302020-07-302020-07-3010002391Cart Services Pvt. Ltd. _ Bangalore _Venkatapura Village MalurFMCGSouthRENTALBangaloreKarnatakaKA02Pallet Company India Bangalore-Hobli10002391Cart Services Pvt. Ltd. - Bangalore (Venkatapura Village, Malur )AllotPallet CompanyOUT12000.0Pallet Company Transport borne by customerWooden PalletA010000003300Nos15002020/19872020-07-302020-07-31CRentEcommerceAllot32ft SXLNNaN0.0NaNWooden Pallet3
102020-08-102020-08-102020-08-1010005874Cart Services Private Limited _ Bhiwandi_ Saidham, G WarehouseFMCGWestRENTALThaneMaharashtraMH03Pallet Company India Bhiwandi10005874Cart Services Private Limited _ Bhiwandi_ Saidham, G WarehouseAllotPallet CompanyOUT5000.0Pallet Company Transport borne by customerWooden PalletA010000003300Nos15002020/22942020-08-072020-08-21CRentEcommerceAllot32ft SXLNNaNNaNNaNWooden Pallet3